IFDS

SILO: Jose Blanchet
2021-02-24 12:30 - 13:30 (America/Chicago)
Title: TBD

IFDS Ideas Forum: Blake Mason
2021-02-22 13:30 - 14:30 (America/Chicago)
PERSON: Blake Mason
TITLE: Metric Learning from Labelled Data is Really Hard
ABSTRACT:
BIO:

ML-Opt @ UWash: Omid Sadeghi
2021-02-19 13:30 - 14:30 (America/Chicago)
Title:
Abstract:

SILO: Tara Javidi
2021-02-17 12:30 - 13:30 (America/Chicago)
Generalization to Natural Out-of-Distribution Inputs
UNTIL FURTHER NOTICE: Seminars are virtual. Sign up for the SILO email list to receive the links to each talk at https://groups.google.com/ and browse for silo

IFDS Ideas Forum: Steve Wright
2021-02-15 13:30 - 14:30 (America/Chicago)
PERSON: Steve Wright
TITLE: Discussion: Zero-Loss NN training
ABSTRACT:
BIO:

ML-Opt @ UWash: Postponed-TBD
2021-02-12 13:30 - 14:30 (America/Chicago)
This talk will be rescheduled for a later date.
Title: Convergence of the empirical superquantile for risk-sensitive machine learning
Abstract: I’ll present some background in risk-sensitive learning + Theorem 2.1 of this paper (using ML nomenclature)

SILO: Hamed Hasani
2021-02-10 12:30 - 13:30 (America/Chicago)
Two Facets of Learning Robust Models: Fundamental Limits and
UNTIL FURTHER NOTICE: Seminars are virtual. Sign up for the SILO email list to receive the links to each talk at https://groups.google.com/ and browse for silo

IFDS Ideas Forum: Miaoyan Wang
2021-02-08 13:30 - 14:30 (America/Chicago)
PERSON: Miaoyan Wang
TITLE: Beyond the Signs: Nonparametric Tensor Completion via Sign Series
ABSTRACT:
BIO: I am an assistant professor in the Department of Statistics at the University of Wisconsin-Madison since late 2018. I am also an affiliate of the Institute for Foundations of Data Science (IFDS) at Wisconsin Institute for Discovery (WID). My research is in statistical machine learning with a particular focus on matrix/tensor data analysis. Other interests include statistical applications in genetics and neuroimaging.
Prior to UW-Madision, I was a postdoc in Computer Science (EECS Department) at UC Berkeley and a Simons Math+X postdoc at University of Pennsylvania. I received my PhD in Statistics from the University of Chicago and BS in Mathematics from Fudan University.

IFDS All-Hands: Zaid Harchaoui
2021-02-05 13:30 - 14:30 (America/Los_Angeles)
Zaid Harchaoui, Associate Professor, Statistics
Title: On statistical estimation, signal denoising, and convex optimization
Abstract: We revisit the classical statistical problem of adaptive discrete-time signal denoising. Conventional nonparametric statistics and signal processing approaches rely on strong structural assumptions on the signal set. The approach we present, inspired by the seminal works from Ibragimov-Khasminskii and later Donoho, takes a different view and focuses instead on statistical estimators amenable to convex optimization. We show that such estimators possess better statistical properties than conventional ones. In particular, under an assumption of approximate shift-invariance, the proposed estimators enjoy l2-loss oracle inequalities. We show how to implement them using optimal first-order methods and highlight interesting statistical-computational trade-offs. Joint work with D. Ostrovskii, A. Juditsky, A. Nemirovski.

SILO: Yao Xie
2021-02-03 12:30 - 13:30 (America/Chicago)
Statistical inference on spatio-temporal networks
UNTIL FURTHER NOTICE: Seminars are virtual. Sign up for the SILO email list to receive the links to each talk at https://groups.google.com/ and browse for silo

ML-Opt @ UWash: Ronak Mehta
2021-01-29 13:30 - 14:30 (America/Chicago)

IFDS Ideas Forum: Xuexhou Zhang
2021-01-25 13:30 - 14:30 (America/Chicago) | Webex
Title: Statistical Robustness in Reinforcement Learning
Abstract:
Xuezhou Zhang (Computer Sciences), advised by Jerry Zhu (Computer Sciences) and Kevin Jamieson (U of Washington), works on adaptivity and robustness in sequential decision making. His recent work focuses on designing reinforcement learning framework that learns from a diverse source of teaching signals and learns robustly in the presence of data corruptions.

ML-Opt @ UWash: Lang Liu
2021-01-22 13:30 - 14:30 (America/Los_Angeles)

ML-Opt @ U Washington: Yue Yu
2021-01-15 13:30 - 14:30 (America/Los_Angeles)
Title: Proportional-integral projected gradient method for model predictive control
Abstract:

IFDS Ideas Forum
2013-03-27 14:00 (America/Chicago)
Current research

Sushrut Karmalkar
2026-06-29 12:58 (America/Chicago)

Jitian Zhao / Zhexuan Liu
2026-06-29 12:58 (America/Chicago)

Thanasis Pittas
2026-06-29 12:58 (America/Chicago)

Karan Srivastava / Chenghui Li
2026-06-29 12:58 (America/Chicago)

William Powell / Andrew Lowy
2026-06-29 12:58 (America/Chicago)

Hanbaek Lyu / Ahmet Alacaoglu
2026-06-29 12:58 (America/Chicago)

Ziqian Lin/ Lijun Ding
2026-06-29 12:58 (America/Chicago)

Harit Vishwakarma/Ramya Vinayak
2026-06-29 12:58 (America/Chicago)

Joe Shenouda/Matthew Zurek
2026-06-29 12:58 (America/Chicago)

Nicholas Roberts
2026-06-29 12:58 (America/Chicago)

Alex Hayes
2026-06-29 12:58 (America/Chicago)

Shi Chen
2026-06-29 12:58 (America/Chicago)

Ernest Ryu
2026-06-29 12:58 (America/Chicago)

Sixu Li/Yuchen Zeng
2026-06-29 12:58 (America/Chicago)

Dimitris Papailiopoulos / Nayoung Lee
2026-06-29 12:58 (America/Chicago)
